import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "deta"
							UpperCAmelCase				   =     {
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}







							def __init__(   self      :     Optional[int] ,			_A      :     Tuple=None ,			_A      :     Dict=900 ,			_A      :     Union[str, Any]=2048 ,			_A      :     Union[str, Any]=6 ,			_A      :     List[str]=2048 ,			_A      :     str=8 ,			_A      :     Optional[int]=6 ,			_A      :     List[str]=1024 ,			_A      :     Optional[int]=8 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     Any="relu" ,			_A      :     Any=256 ,			_A      :     Optional[int]=0.1 ,			_A      :     str=0.0 ,			_A      :     Dict=0.0 ,			_A      :     str=0.02 ,			_A      :     Union[str, Any]=1.0 ,			_A      :     Union[str, Any]=True ,			_A      :     Any=False ,			_A      :     Union[str, Any]="sine" ,			_A      :     int=5 ,			_A      :     Optional[Any]=4 ,			_A      :     Any=4 ,			_A      :     Union[str, Any]=True ,			_A      :     Dict=300 ,			_A      :     List[Any]=True ,			_A      :     Any=True ,			_A      :     Tuple=1 ,			_A      :     Optional[int]=5 ,			_A      :     str=2 ,			_A      :     Tuple=1 ,			_A      :     Tuple=1 ,			_A      :     Any=5 ,			_A      :     Tuple=2 ,			_A      :     str=0.1 ,			_A      :     List[str]=0.25 ,			**_A      :     Dict ,			):
												if backbone_config is None:
																	logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''		)
																	_UpperCamelCase			     =     CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']		)
												else:
																	if isinstance(_A ,			_A		):
																						_UpperCamelCase			     =     backbone_config.pop('''model_type'''		)
																						_UpperCamelCase			     =     CONFIG_MAPPING[backbone_model_type]
																						_UpperCamelCase			     =     config_class.from_dict(_A		)

												_UpperCamelCase			     =     backbone_config
												_UpperCamelCase			     =     num_queries
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     encoder_ffn_dim
												_UpperCamelCase			     =     encoder_layers
												_UpperCamelCase			     =     encoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     init_xavier_std
												_UpperCamelCase			     =     encoder_layerdrop
												_UpperCamelCase			     =     auxiliary_loss
												_UpperCamelCase			     =     position_embedding_type
												# deformable attributes
												_UpperCamelCase			     =     num_feature_levels
												_UpperCamelCase			     =     encoder_n_points
												_UpperCamelCase			     =     decoder_n_points
												_UpperCamelCase			     =     two_stage
												_UpperCamelCase			     =     two_stage_num_proposals
												_UpperCamelCase			     =     with_box_refine
												_UpperCamelCase			     =     assign_first_stage
												if two_stage is True and with_box_refine is False:
																	raise ValueError('''If two_stage is True, with_box_refine must be True.'''		)
												# Hungarian matcher
												_UpperCamelCase			     =     class_cost
												_UpperCamelCase			     =     bbox_cost
												_UpperCamelCase			     =     giou_cost
												# Loss coefficients
												_UpperCamelCase			     =     mask_loss_coefficient
												_UpperCamelCase			     =     dice_loss_coefficient
												_UpperCamelCase			     =     bbox_loss_coefficient
												_UpperCamelCase			     =     giou_loss_coefficient
												_UpperCamelCase			     =     eos_coefficient
												_UpperCamelCase			     =     focal_alpha
												super().__init__(is_encoder_decoder=_A ,			**_A		)







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												return self.encoder_attention_heads







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return self.d_model







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     copy.deepcopy(self.__dict__		)
												_UpperCamelCase			     =     self.backbone_config.to_dict()
												_UpperCamelCase			     =     self.__class__.model_type
												return output




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()

						parser.add_argument(
						    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
						)
						# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
						parser.add_argument(
						    '--original_config_file',
						    default=None,
						    type=str,
						    help='The YAML config file corresponding to the original architecture.',
						)
						parser.add_argument(
						    '--num_in_channels',
						    default=None,
						    type=int,
						    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
						)
						parser.add_argument(
						    '--scheduler_type',
						    default='pndm',
						    type=str,
						    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
						)
						parser.add_argument(
						    '--pipeline_type',
						    default=None,
						    type=str,
						    help=(
						        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
						        '. If `None` pipeline will be automatically inferred.'
						    ),
						)
						parser.add_argument(
						    '--image_size',
						    default=None,
						    type=int,
						    help=(
						        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
						        ' Base. Use 768 for Stable Diffusion v2.'
						    ),
						)
						parser.add_argument(
						    '--prediction_type',
						    default=None,
						    type=str,
						    help=(
						        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
						        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
						    ),
						)
						parser.add_argument(
						    '--extract_ema',
						    action='store_true',
						    help=(
						        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
						        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
						        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
						    ),
						)
						parser.add_argument(
						    '--upcast_attention',
						    action='store_true',
						    help=(
						        'Whether the attention computation should always be upcasted. This is necessary when running stable'
						        ' diffusion 2.1.'
						    ),
						)
						parser.add_argument(
						    '--from_safetensors',
						    action='store_true',
						    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
						)
						parser.add_argument(
						    '--to_safetensors',
						    action='store_true',
						    help='Whether to store pipeline in safetensors format or not.',
						)
						parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
						parser.add_argument(
						    '--stable_unclip',
						    type=str,
						    default=None,
						    required=False,
						    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
						)
						parser.add_argument(
						    '--stable_unclip_prior',
						    type=str,
						    default=None,
						    required=False,
						    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
						)
						parser.add_argument(
						    '--clip_stats_path',
						    type=str,
						    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
						    required=False,
						)
						parser.add_argument(
						    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
						)
						parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
						parser.add_argument(
						    '--vae_path',
						    type=str,
						    default=None,
						    required=False,
						    help='Set to a path, hub id to an already converted vae to not convert it again.',
						)
						__UpperCamelCase     		=							parser.parse_args()

						__UpperCamelCase     		=							download_from_original_stable_diffusion_ckpt(
						    checkpoint_path=args.checkpoint_path,
						    original_config_file=args.original_config_file,
						    image_size=args.image_size,
						    prediction_type=args.prediction_type,
						    model_type=args.pipeline_type,
						    extract_ema=args.extract_ema,
						    scheduler_type=args.scheduler_type,
						    num_in_channels=args.num_in_channels,
						    upcast_attention=args.upcast_attention,
						    from_safetensors=args.from_safetensors,
						    device=args.device,
						    stable_unclip=args.stable_unclip,
						    stable_unclip_prior=args.stable_unclip_prior,
						    clip_stats_path=args.clip_stats_path,
						    controlnet=args.controlnet,
						    vae_path=args.vae_path,
						)

						if args.half:
												pipe.to(torch_dtype=torch.floataa)

						if args.controlnet:
												# only save the controlnet model
												pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
						else:
												pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


